Martingale Detection of Treatment Effects in Wound Care

Oskar Gustafsson, John Pavia, Daniel Tsang, Ernst Ahlberg
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:1057-1059, 2026.

Abstract

In clinical research, the ability to act on accumulating evidence motivates methods that support valid sequential decision-making. We apply a martingale-based framework for detecting treatment effects in a one-arm clinical trial in wound care, where outcomes are continuously compared against a reference distribution. We then extend this framework to individualized treatment evaluation by constructing patient-specific counterfactual trajectories from a real-world reference cohort using Gaussian kernel weighting. Because the underlying null distribution is not fully known, treatment effects are quantified through a martingale-inspired divergence measure, and statistical significance is assessed using an empirical null distribution generated from 5,000 resampled reference cohorts.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-gustafsson26c, title = {Martingale Detection of Treatment Effects in Wound Care}, author = {Gustafsson, Oskar and Pavia, John and Tsang, Daniel and Ahlberg, Ernst}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {1057--1059}, year = {2026}, editor = {Ahlberg, Ernst and Johansson, Ulf and Boström, Henrik and Carlevaro, Alberto and Hallberg Szabadváry, Johan and Carlsson, Lars}, volume = {329}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v329/main/assets/gustafsson26c/gustafsson26c.pdf}, url = {https://proceedings.mlr.press/v329/gustafsson26c.html}, abstract = {In clinical research, the ability to act on accumulating evidence motivates methods that support valid sequential decision-making. We apply a martingale-based framework for detecting treatment effects in a one-arm clinical trial in wound care, where outcomes are continuously compared against a reference distribution. We then extend this framework to individualized treatment evaluation by constructing patient-specific counterfactual trajectories from a real-world reference cohort using Gaussian kernel weighting. Because the underlying null distribution is not fully known, treatment effects are quantified through a martingale-inspired divergence measure, and statistical significance is assessed using an empirical null distribution generated from 5,000 resampled reference cohorts.} }
Endnote
%0 Conference Paper %T Martingale Detection of Treatment Effects in Wound Care %A Oskar Gustafsson %A John Pavia %A Daniel Tsang %A Ernst Ahlberg %B Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2026 %E Ernst Ahlberg %E Ulf Johansson %E Henrik Boström %E Alberto Carlevaro %E Johan Hallberg Szabadváry %E Lars Carlsson %F pmlr-v329-gustafsson26c %I PMLR %P 1057--1059 %U https://proceedings.mlr.press/v329/gustafsson26c.html %V 329 %X In clinical research, the ability to act on accumulating evidence motivates methods that support valid sequential decision-making. We apply a martingale-based framework for detecting treatment effects in a one-arm clinical trial in wound care, where outcomes are continuously compared against a reference distribution. We then extend this framework to individualized treatment evaluation by constructing patient-specific counterfactual trajectories from a real-world reference cohort using Gaussian kernel weighting. Because the underlying null distribution is not fully known, treatment effects are quantified through a martingale-inspired divergence measure, and statistical significance is assessed using an empirical null distribution generated from 5,000 resampled reference cohorts.
APA
Gustafsson, O., Pavia, J., Tsang, D. & Ahlberg, E.. (2026). Martingale Detection of Treatment Effects in Wound Care. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:1057-1059 Available from https://proceedings.mlr.press/v329/gustafsson26c.html.

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